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PrototypeML: Visual Design of Arbitrarily Complex Neural Networks

Daniel Harris


Neural network architectures are most often conceptually designed and described in visual terms, but are implemented by writing error-prone code. PrototypeML is a neural network development environment that bridges the dichotomy between the design and development processes: it provides a highly intuitive visual neural network design interface that supports (yet abstracts) the full dynamic graph capabilities of the PyTorch deep learning framework, reduces model design and development time, makes debugging easier, and automates many framework and code writing idiosyncrasies. Through a hybrid code and visual approach, PrototypeML resolves deep learning development deficiencies without limiting network expressiveness or reducing code quality, and provides real-world benefits for research, industry and teaching.

Join us for a live overview (and Q&A) of the PrototypeML platform during the conference, and explore the on-demand interactive platform demonstration:

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